戒酒的李白
275 lines
11 KiB
Python
275 lines
11 KiB
Python
import time
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import openai
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import pandas as pd
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from tqdm import tqdm
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from scipy.sparse import csr_matrix
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from typing import Mapping, List, Tuple, Any, Union, Callable
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from bertopic.representation._base import BaseRepresentation
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from bertopic.representation._utils import (
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retry_with_exponential_backoff,
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truncate_document,
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validate_truncate_document_parameters,
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)
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DEFAULT_CHAT_PROMPT = """You will extract a short topic label from given documents and keywords.
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Here are two examples of topics you created before:
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# Example 1
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Sample texts from this topic:
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- Traditional diets in most cultures were primarily plant-based with a little meat on top, but with the rise of industrial style meat production and factory farming, meat has become a staple food.
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- Meat, but especially beef, is the worst food in terms of emissions.
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- Eating meat doesn't make you a bad person, not eating meat doesn't make you a good one.
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Keywords: meat beef eat eating emissions steak food health processed chicken
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topic: Environmental impacts of eating meat
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# Example 2
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Sample texts from this topic:
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- I have ordered the product weeks ago but it still has not arrived!
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- The website mentions that it only takes a couple of days to deliver but I still have not received mine.
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- I got a message stating that I received the monitor but that is not true!
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- It took a month longer to deliver than was advised...
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Keywords: deliver weeks product shipping long delivery received arrived arrive week
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topic: Shipping and delivery issues
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# Your task
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Sample texts from this topic:
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[DOCUMENTS]
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Keywords: [KEYWORDS]
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Based on the information above, extract a short topic label (three words at most) in the following format:
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topic: <topic_label>
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"""
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DEFAULT_SYSTEM_PROMPT = "You are an assistant that extracts high-level topics from texts."
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class OpenAI(BaseRepresentation):
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r"""Using the OpenAI API to generate topic labels based
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on one of their Completion of ChatCompletion models.
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For an overview see:
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https://platform.openai.com/docs/models
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Arguments:
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client: A `openai.OpenAI` client
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model: Model to use within OpenAI, defaults to `"gpt-4o-mini"`.
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generator_kwargs: Kwargs passed to `openai.Completion.create`
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for fine-tuning the output.
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prompt: The prompt to be used in the model. If no prompt is given,
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`self.default_prompt_` is used instead.
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NOTE: Use `"[KEYWORDS]"` and `"[DOCUMENTS]"` in the prompt
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to decide where the keywords and documents need to be
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inserted.
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system_prompt: The system prompt to be used in the model. If no system prompt is given,
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`self.default_system_prompt_` is used instead.
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delay_in_seconds: The delay in seconds between consecutive prompts
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in order to prevent RateLimitErrors.
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exponential_backoff: Retry requests with a random exponential backoff.
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A short sleep is used when a rate limit error is hit,
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then the requests is retried. Increase the sleep length
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if errors are hit until 10 unsuccessful requests.
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If True, overrides `delay_in_seconds`.
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nr_docs: The number of documents to pass to OpenAI if a prompt
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with the `["DOCUMENTS"]` tag is used.
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diversity: The diversity of documents to pass to OpenAI.
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Accepts values between 0 and 1. A higher
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values results in passing more diverse documents
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whereas lower values passes more similar documents.
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doc_length: The maximum length of each document. If a document is longer,
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it will be truncated. If None, the entire document is passed.
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tokenizer: The tokenizer used to calculate to split the document into segments
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used to count the length of a document.
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* If tokenizer is 'char', then the document is split up
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into characters which are counted to adhere to `doc_length`
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* If tokenizer is 'whitespace', the document is split up
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into words separated by whitespaces. These words are counted
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and truncated depending on `doc_length`
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* If tokenizer is 'vectorizer', then the internal CountVectorizer
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is used to tokenize the document. These tokens are counted
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and truncated depending on `doc_length`
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* If tokenizer is a callable, then that callable is used to tokenize
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the document. These tokens are counted and truncated depending
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on `doc_length`
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Usage:
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To use this, you will need to install the openai package first:
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`pip install openai`
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Then, get yourself an API key and use OpenAI's API as follows:
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```python
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import openai
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from bertopic.representation import OpenAI
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from bertopic import BERTopic
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# Create your representation model
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client = openai.OpenAI(api_key=MY_API_KEY)
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representation_model = OpenAI(client, delay_in_seconds=5)
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# Use the representation model in BERTopic on top of the default pipeline
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topic_model = BERTopic(representation_model=representation_model)
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```
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You can also use a custom prompt:
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```python
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prompt = "I have the following documents: [DOCUMENTS] \nThese documents are about the following topic: '"
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representation_model = OpenAI(client, prompt=prompt, delay_in_seconds=5)
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```
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To choose a model:
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```python
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representation_model = OpenAI(client, model="gpt-4o-mini", delay_in_seconds=10)
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```
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"""
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def __init__(
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self,
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client,
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model: str = "gpt-4o-mini",
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prompt: str = None,
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system_prompt: str = None,
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generator_kwargs: Mapping[str, Any] = {},
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delay_in_seconds: float = None,
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exponential_backoff: bool = False,
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nr_docs: int = 4,
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diversity: float = None,
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doc_length: int = None,
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tokenizer: Union[str, Callable] = None,
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**kwargs,
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):
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self.client = client
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self.model = model
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if prompt is None:
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self.prompt = DEFAULT_CHAT_PROMPT
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else:
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self.prompt = prompt
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if system_prompt is None:
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self.system_prompt = DEFAULT_SYSTEM_PROMPT
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else:
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self.system_prompt = system_prompt
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self.default_prompt_ = DEFAULT_CHAT_PROMPT
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self.default_system_prompt_ = DEFAULT_SYSTEM_PROMPT
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self.delay_in_seconds = delay_in_seconds
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self.exponential_backoff = exponential_backoff
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self.nr_docs = nr_docs
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self.diversity = diversity
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self.doc_length = doc_length
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self.tokenizer = tokenizer
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validate_truncate_document_parameters(self.tokenizer, self.doc_length)
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self.prompts_ = []
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self.generator_kwargs = generator_kwargs
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if self.generator_kwargs.get("model"):
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self.model = generator_kwargs.get("model")
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del self.generator_kwargs["model"]
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if self.generator_kwargs.get("prompt"):
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del self.generator_kwargs["prompt"]
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if not self.generator_kwargs.get("stop"):
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self.generator_kwargs["stop"] = "\n"
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def extract_topics(
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self,
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topic_model,
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documents: pd.DataFrame,
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c_tf_idf: csr_matrix,
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topics: Mapping[str, List[Tuple[str, float]]],
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) -> Mapping[str, List[Tuple[str, float]]]:
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"""Extract topics.
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Arguments:
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topic_model: A BERTopic model
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documents: All input documents
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c_tf_idf: The topic c-TF-IDF representation
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topics: The candidate topics as calculated with c-TF-IDF
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Returns:
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updated_topics: Updated topic representations
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"""
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# Extract the top n representative documents per topic
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repr_docs_mappings, _, _, _ = topic_model._extract_representative_docs(
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c_tf_idf, documents, topics, 500, self.nr_docs, self.diversity
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)
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# Generate using OpenAI's Language Model
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updated_topics = {}
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for topic, docs in tqdm(repr_docs_mappings.items(), disable=not topic_model.verbose):
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truncated_docs = [truncate_document(topic_model, self.doc_length, self.tokenizer, doc) for doc in docs]
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prompt = self._create_prompt(truncated_docs, topic, topics)
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self.prompts_.append(prompt)
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# Delay
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if self.delay_in_seconds:
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time.sleep(self.delay_in_seconds)
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messages = [
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{"role": "system", "content": self.system_prompt},
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{"role": "user", "content": prompt},
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]
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kwargs = {
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"model": self.model,
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"messages": messages,
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**self.generator_kwargs,
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}
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if self.exponential_backoff:
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response = chat_completions_with_backoff(self.client, **kwargs)
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else:
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response = self.client.chat.completions.create(**kwargs)
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# Check whether content was actually generated
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# Addresses #1570 for potential issues with OpenAI's content filter
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# Addresses #2176 for potential issues when openAI returns a None type object
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if response and hasattr(response.choices[0].message, "content"):
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label = response.choices[0].message.content.strip().replace("topic: ", "")
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else:
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label = "No label returned"
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updated_topics[topic] = [(label, 1)]
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return updated_topics
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def _create_prompt(self, docs, topic, topics):
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keywords = list(zip(*topics[topic]))[0]
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# Use the Default Chat Prompt
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if self.prompt == DEFAULT_CHAT_PROMPT:
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prompt = self.prompt.replace("[KEYWORDS]", ", ".join(keywords))
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prompt = self._replace_documents(prompt, docs)
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# Use a custom prompt that leverages keywords, documents or both using
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# custom tags, namely [KEYWORDS] and [DOCUMENTS] respectively
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else:
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prompt = self.prompt
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if "[KEYWORDS]" in prompt:
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prompt = prompt.replace("[KEYWORDS]", ", ".join(keywords))
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if "[DOCUMENTS]" in prompt:
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prompt = self._replace_documents(prompt, docs)
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return prompt
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@staticmethod
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def _replace_documents(prompt, docs):
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to_replace = ""
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for doc in docs:
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to_replace += f"- {doc}\n"
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prompt = prompt.replace("[DOCUMENTS]", to_replace)
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return prompt
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def chat_completions_with_backoff(client, **kwargs):
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return retry_with_exponential_backoff(
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client.chat.completions.create,
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errors=(openai.RateLimitError,),
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)(**kwargs)
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